Deep Residual Echo Suppression with A Tunable Tradeoff Between Signal Distortion and Echo Suppression

TL;DR

This paper introduces a neural network-based residual echo suppression method with a tunable parameter to balance echo suppression and signal distortion, optimized for real-time on-device applications.

Contribution

A novel UNet-based residual echo suppression system with a tunable tradeoff parameter, suitable for real-time, on-device acoustic echo cancellation scenarios.

Findings

Effective echo suppression in real-life conditions

Tunable parameter balances echo suppression and signal distortion

Competitive performance compared to existing methods

Abstract

In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain. This system embeds a design parameter that allows a tunable tradeoff between the desired-signal distortion and residual echo suppression in double-talk scenarios. The system employs 136 thousand parameters, and requires 1.6 Giga floating-point operations per second and 10 Mega-bytes of memory. The implementation satisfies both the timing requirements of the AEC challenge and the computational and memory limitations of on-device applications. Experiments are conducted with 161~h of data from the AEC challenge database and from real independent recordings. We demonstrate the performance of the proposed system in real-life conditions and compare it with two competing methods regarding echo suppression and desired-signal distortion, generalization to various environments, and robustness to high echo levels.